Approximately 200,000 women are diagnosed each year in the U.S. with breast cancer, and nearly 50,000 die of their metastatic disease. Significant improvement made in both early detection and local/systemic therapy in the last few decades has significantly improved patient outcomes, especially survival. Breast cancers are characterized by their estrogen and progesterone receptor status (hereon termed ER), and it is established that ER expression (ER+) identifies a tumor phenotype with improved near/mid-term prognosis and likely benefit from adjuvant endocrine therapy when compared to ER-negative (ER−) tumors. Yet, little is known about the genomic features within each ER subtype of breast cancer that could explain why some patients with the same ER status have a good outcome while others do poorly regardless of treatment.
Current decision algorithms based on standard clinicopathologic factors stratify ER− disease as having a high-risk for recurrence. Although patients are now routinely offered adjuvant chemotherapy, most patients with node-negative, ER− disease remain disease-free after local therapy alone, including approximately 80% of ER− patients with tumors ≤1 cm and up to 60% of all with stage 1 disease. Consequently, there are patients with ER− disease that might do well without adjuvant chemotherapy and could avoid its potential toxicities, while others with a high residual risk despite it might be offered trials of novel therapies. Unfortunately, existing markers routinely used in clinical practice are of limited or no use in ER− patients. For example, commonly used gene expression tests by RT-PCR have no clear prognostic/predictive utility in ER− disease and microarray assays developed so far appear to identify essentially all such patients as high risk, while other markers are still in development. Consequently, there is a critical need to develop better prognostic factors to improve assessment of residual risk and better predictive markers to optimize patient selection for standard and investigational systemic therapies.
Methylated genes are particularly robust as biomarkers. In past studies, the present inventors developed a cancer detection panel using a quantitative cumulative methylation assay known as Quantitative Multiplex-Methylation Specific PCR (QM-MSP) (see, U.S. Pat. No. 8,062,849) wherein the methylation status of multiple genes could be determined individually and cumulatively from picograms of input DNA, such as is retrieved from ductal lavage or ductoscopy and pathologic nipple discharge fluid. It has also been found that methylated genes are frequently detected in the pre-invasive stage of DCIS. Further, histopathologically normal ducts in the vicinity of tumor tissue display detectable hypermethylation of genes that are present in the adjacent DCIS or invasive cancer, while normal ducts present farther away do not. However, using the candidate marker approach it has been difficult to identify markers informative of the biology specifically of ER-positive or negative breast cancer or those that predict response to therapy, disease progression and survival. Therefore, there still exists a need for a genome-wide discovery platform would identify gene loci in tumors that better predict clinical outcomes.